We adapted a range of 11 unique baselines and FSL methods: Fine-tune baseline [1]}, 1-NN baseline, Baseline++ [2]}, SimpleShot [3]}, Prototypical Networks [4]}, Matching Networks [5]}, Relation Networks [6]}, MAML [7]}, ProtoMAML [8]}, DKT [9]}, and Bayesian MAML (BMAML) [10]}. Implementation details of these algorithms are supplied in Appendix . We used a 4 layer convolutional network as backbone for each model, following common practice [2]}. We train and evaluate all methods on MiniImageNet [12]}, [5]}, containing 64 classes with 600 image samples each. In the imbalanced meta-dataset setting, we half the Mini-ImageNet dataset to contain 300 samples per class on average, and control imbalance as described in section REF . For full implementation details, see Appendix .
